On the Study of Lithium Diffusivity in Lithium Nickel Manganese Cobalt Oxide Cathodes
Bibliographic record
Abstract
Climate change, population growth, and rising fossil fuel prices have encouraged governments and scientists for alternate energy resources. This energy transition requires a high-performance energy storage device to satisfy the high energy and power demand and lithium-ion battery (LIB) is one of the promising one. The performance of these batteries ultimately relies on the properties of their components. In this regard, to meet the high-power demand in high-power applications (such as electric vehicles (EVs) and hybrid EVs), materials with rapid lithium transport are required. Lithium Nickel Manganese Cobalt Oxide (NMC) has attracted scientists’ attentions due to its outstanding performance as a cathode material. Therefore, understanding the effect of various factors on lithium diffusivity in NMC is critical to develop high-performance LIBs for high-power applications. Electrochemical methods such as potentiostatic and galvanostatic intermittent titration techniques (PITT and GITT) have been frequently utilized to experimentally quantify lithium diffusivity in NMC. These techniques need the knowledge of electrode particle shape and dimension, and uncertainty about these parameters leads to substantial errors in predicting the diffusion coefficient. In addition, because these techniques consider the response of the whole electrochemical cell, it is hard to distinguish the effect of different structural factors on Li diffusivity in a single NMC active material. Therefore, an appropriate method still needs to be developed to capture the structural effects on lithium diffusivity in NMC. For this purpose, a multi-level modelling from Density Functional Theory (DFT) to kinetic Monte Carlo (KMC) should be implemented. In this study, we will use DFT to find the ground state energy of NMC at different lithium concentrations and configurations. Also, the minimum energy path of lithium migration and the related activation barrier will be found by Climbing Image-Nudge Elastic Band (CI-NEB) method. Then by implementing the configurational dependent activation barrier into the KMC simulation, the lithium diffusivity will be studied. This atomistic simulation gives insight about the structural effects on lithium diffusivity in NMC to further develop this cathode material for high performance LIBs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".